Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title INTELLIGENT SYSTEMS
Code CKIT533
Coordinator Dr F Grasso
Computer Science
Floriana@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 7 FHEQ Whole Session 15

Aims

1 . To provide students with a comprehensive understanding of intelligent systems techniques.

2. To enable students to evaluate modern techniques of artificial intelligence and machine learning for intelligent system projects.

3. To provide students with the knowledge and skills required to develop and deploy expert systems and artificial intelligent tools.


Learning Outcomes

(LO1) An ability to analyse and evaluate intelligent systems techniques.

(LO2) A comprehensive understanding of the differences between intelligent system applications and conventional computer applications.

(LO3) A critical ability to deploy appropriate software tools and skills for the design and implementation of intelligent systems.

(LO4) An in depth understanding of the practical application of the fundamental principles of intelligent systems.

(LO5) An ability to analyse intelligent system problems and formulate appropriate solutions.

(S1) Communication skills

(S2) IT skills

(S3) Communication and collaboration online participating in digital networks for learning and research.

(S4) Problem solving/critical thinking/creativity

(S5) Team (group) working respecting others, co-operating, negotiating / persuading, awareness of interdependence with others


Syllabus

 

Week 1: Introduction to Intelligent systems:
The history of intelligent systems and their importance in real life applications, the characteristics of intelligent systems that serve to segregate them from other systems.

Week 2: Evolutionary Computation Algorithms :
Evolutionary computation algorithms as a sub-field of Artificial Intelligence with an emphasis on Genetic Algorithms (GAs), their structure and application.

Week 3: Rule-based Expert Systems :
How knowledge can be learnt, expressed and represented in the form of production rules, the characteristics of expert systems, forward and backward chaining inference techniques.

Week 4: Fuzzy Expert Systems:
The concept of fuzzy logic and its theoretical underpinning, fuzzy sets and rules, fuzzy inference techniques and the main steps in developing fuzzy expert systems.

Week 5: Artificial Neural Networks :
Artificial neural networks and perceptrons, the back propagat ion and feed forward neural network algorithms.

Week 6: Deep Reinforcement Learning :
Deep neural networks and their applications, available deep learning tools for complex datasets.

Week 7: Hybrid Intelligent Systems:
Hybrid intelligent systems such as neuro-fuzzy systems, evolutionary neural networks and fuzzy evolutionary systems.

Week 8: Intelligent Systems Applications.
The application of intelligent systems in the context of: decision support, classification, decision trees, pattern recognition and data mining.


Teaching and Learning Strategies

Teaching Method 1 - online Learning
Description: Weekly seminar supported by asynchronous discussion in a virtual classroom environment facilitated by an online instructor.
Attendance Recorded: Yes
Notes: Number of hours per week that students are expected to attend the virtual classroom so as to participate in discussion, dedicated to group work and individual assessment is 7.5


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           60

60
Timetable (if known)              
Private Study 90
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Report: Group Work on Intelligent Systems Application Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 8    16       
Case Study Analysis: Reinforcement Learning Standard UoL penalty applies for late submission. Assessment Schedule (When) :Week 6    10       
Case Study Analysis: Artificial Neural Networks Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 5    10       
Case Study Analysis: Fuzzy Expert Systems Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 4    10       
Individual presentation: Expert systems Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 3         
Individual presentation: Comparison of intelligent and conventional systems Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week         
Moot/debate: 8 discussion questions Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Whole session    40       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.